Interactive method and apparatus based on deep question and answer

ABSTRACT

The present disclosure provides an interactive method and an interactive apparatus based on deep question and answer. The interactive method includes: receiving a query; extracting a logic tag of the query, extracting a keyword of the query, and acquiring a search result corresponding to the logic tag and the keyword based on a logical structure data table; and displaying the search result.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is based upon and claims priority to ChinesePatent Application No. 201611230209.2, filed on Dec. 27, 2016, theentirety contents of which are incorporated herein by reference.

FIELD

The present disclosure relates to a field of natural language processingtechnology, and more particularly to an interactive method and aninteractive device based on deep question and answer.

BACKGROUND

Deep question and answer is technology for understanding human'slanguage, intelligent identifying meanings of a question, and extractingan answer to the question from massive internet data.

Users may have requirements for asking questions such as “what does bluethin mushroom means”, “detail information about Nie Shubin's case” and“who is Luo Jin” during a process of reading news. Although the user mayacquire answers by searching for related webpages with the help of asearch engine, searching and browsing the webpages may cost a long time,the efficiency is low and the results are not precise enough.

SUMMARY

Embodiments of the present disclosure seek to solve at least one of theproblems existing in the related art to at least some extent.

Accordingly, a first objective of the present disclosure is to providean interactive method based on deep question and answer. The interactivemethod may improve the search efficiency and the precision of the searchresults.

A second objective of the present disclosure is to provide aninteractive apparatus based on deep question and answer.

In order to achieve the above objectives, embodiments of a first aspectof the present disclosure provide an interactive method based on deepquestion and answer. The interactive method includes: receiving a query;extracting a logic tag of the query, extracting a keyword of the query,and acquiring a search result corresponding to the logic tag and thekeyword based on a logical structure data table; and displaying thesearch result.

In order to achieve the above objectives, embodiments of a second aspectof the present disclosure provide an interactive apparatus based on deepquestion and answer. The interactive apparatus includes: a receivingmodule, configured to receive a query; an acquiring module, configuredto extract a logic tag of the query, to extract a keyword of the query,and to acquire a search result corresponding to the logic tag and thekeyword based on a logical structure data table; and a displayingmodule, configured to display the search result.

Embodiments of the present disclosure also provide a device, including:one or more processors; a memory for storing one or more programs; whenthe one or more programs are executed by the one or more processors, theone or more processors are configured to execute the method according toany of embodiments of the first aspect of the present disclosure.

Embodiments of the present disclosure also provide a non-transitorycomputer readable storage medium for storing one or more applicationprograms, when the one or more application programs executed by one ormore processors of a device, the one or more processors are configuredto execute the method according to any of the embodiments of the firstaspect of the present disclosure.

Embodiments of the present disclosure also provide a computer programproduct that, when executed by one or more processors of a device,causes the one or more processors to execute the method according to anyof the embodiments of the first aspect of the present disclosure.

Additional aspects and advantages of embodiments of present disclosurewill be given in part in the following descriptions, become apparent inpart from the following descriptions, or be learned from the practice ofthe embodiments of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects and advantages of embodiments of the presentdisclosure will become apparent and more readily appreciated from thefollowing descriptions made with reference to the drawings, in which:

FIG. 1 is a flow chart of an interactive method based on deep questionand answer according to an embodiment of the present disclosure;

FIG. 2 is a schematic diagram of a logical structure data tableaccording to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of a displaying effect of search resultsaccording to an embodiment of the present disclosure;

FIG. 4 is a flow chart of an interactive method based on deep questionand answer according to another embodiment of the present disclosure;

FIG. 5 is a schematic diagram showing a result of dependency syntaxparsing and a semantic analysis performed on a query according to anembodiment of the present disclosure;

FIG. 6 is schematic diagram showing a result of dependency syntaxparsing and a semantic analysis performed on a non-labeled queryaccording to an embodiment of the present disclosure;

FIG. 7 is schematic diagram showing a result of dependency syntaxparsing and a semantic analysis performed on another non-labeled queryaccording to an embodiment of the present disclosure;

FIG. 8 is a block diagram of an interactive apparatus based on deepquestion and answer according to an embodiment of the presentdisclosure; and

FIG. 9 is a block diagram of an interactive apparatus based on deepquestion and answer according to another embodiment of the presentdisclosure.

DETAILED DESCRIPTION

Reference will be made in detail to embodiments of the presentdisclosure, where the same or similar elements and the elements havingsame or similar functions are denoted by like reference numeralsthroughout the descriptions. The embodiments described herein withreference to drawings are explanatory, illustrative, and used togenerally understand the present disclosure. The embodiments shall notbe construed to limit the present disclosure.

FIG. 1 is a flow chart of an interactive method based on deep questionand answer according to an embodiment of the present disclosure.

As shown in FIG. 1, the interactive method includes the following acts.

In block S11, a query is received.

For example, the user may input the query via a client, the client maysend the query to a server, and the server may receive the query sent bythe client.

In block S12, a logic tag and a keyword of the query are extracted, anda search result corresponding to the logic tag and the keyword isacquired based on a logical structure data table.

Block S12 may be executed by the server.

Logic tags, keywords and corresponding answer data may be recorded inthe logical structure data table. For example, an exemplary logicalstructure data table is shown in FIG. 2, in which each column representsa keyword such as “Luo Jin”, “Fan Bingbing” or the like, each rowrepresents a logic tag such as “introduction”, “background” or the like,and shade parts represent answer data corresponding to the keywords andthe logic tags. For example, the data 23 in FIG. 2 represents the answerdata corresponding to the logic tag 21 “introduction” and the keyword 22“Luo Jin”. The data recorded in the logical structure data table may beacquired based on encyclopedia data. For example, the above desired datamay be acquired by analyzing lexical items of the encyclopedia data.Detailed content of establishing the logical structure data table willbe described below.

Corresponding data may be found in the logical structure data tableafter the logic tag and the keyword of the query are extracted. Forexample, the data 23 is regarded as the search result according to thelogical structure data table shown in FIG. 2 if the logic tag is“introduction” and the keyword is “Luo Jin”.

It should be noted that some data in the logical structure data tablemay be null and some search results acquired in this case may be nullaccordingly. For example, when the logic tag is “background” and thekeyword is “Luo Jin”, the search result may be null based on the logicalstructure data table, since the data corresponding to “background” and“Luo Jin” is null.

It could be understood that the query input by the user is not limitedto text, the query in other forms may be allowed, such as a speech.Taking a speech input as an example, the speech input may be recognizedon the client or the server, and the text data may be acquired. Theabove extracting process may be executed based on the text data.

In block S13, the search result is displayed.

For example, the server may send the search result to the client afterthe search result is acquired, and the client may display the searchresult for the user.

As shown in FIG. 3, a schematic diagram of a displaying effect of searchresults is provided. Referring to FIG. 3, the search result 32 may bedisplayed for the user after the user input the query 31 “who is LuoJin”.

It should be noted that the search result displayed in related arts maybe link information of the webpage. Instead, the search resultsdisplayed in embodiments of the present disclosure are not linkinformation. Specifically, the search result may include a text and/oran image. For example, the search result in FIG. 3 includes an image andtexts, in other words the search result may be illustrated to the userbased on pictures and their corresponding essays.

It could be understood that the displayed search results are not limitedto texts. For example, the text may be converted into the speech byspeech synthesis, such that the search result may be feedback to theuser in a form of speech in a case of voice-operated application withouta screen.

In this embodiment, the search result instead of the link informationmay be displayed, such that there is no need for the user to open thewebpage to see the results, which means that the search efficiency maybe improved. Additionally, since the search results are acquired basedon the logical structure data table, the logical structure data table isestablished based on the encyclopedia data and the encyclopedia data areauthoritative, the efficiency of the search results may be improved.

FIG. 4 is a flow chart of an interactive method based on deep questionand answer according to another embodiment of the present disclosure.

As shown in FIG. 4, the method of the embodiment also includes thefollowing acts.

In block S401, the logical structure data table is established by theserver based on the encyclopedia data.

The encyclopedia is an open knowledge platform which is edited andmaintained by hundreds and thousands of volunteers and experts invarious fields. For example, there are millions and millions of lexicalitems in Baidu Encyclopedia provided by more than six million volunteersuntil now. The quality of most of the lexical items in BaiduEncyclopedia is higher than the lexical items in common webpages. Inaddition, editing regulations of Baidu Encyclopedia are standard, whichmeans that there are clear logical structures for the lexical itemsrespectively. These features make the encyclopedia data suitable to givethe user precise answers to the query.

The keyword, the logic tag and the search result corresponding to thelogic tag and the keyword are recorded in the logical structure datatable. These data may be determined after the lexical items of theencyclopedia data are analyzed.

Each of the lexical items of the encyclopedia data may be edited in acertain editing regulation. Generally, each lexical item includes atitle, an abstract and a sub-title. The abstract also called“encyclopedia card” is a general introduction, and the sub-title is alogical structure defined in the editing regulation. For example, alexical item of a character may include “experience”, “workachievement”, “evaluation” etc., a lexical item of an event may include“background”, “causes”, “latest developments” etc. HTML documents may beanalyzed when extracting the encyclopedia data, such that the title, theabstract and the sub-title of each lexical item may be acquired.

From each lexical item, the keyword, the logic tag and the answer dataof the lexical item may be extracted, and then the keyword, the logictag and the answer data corresponding to each lexical item may beregarded as an item in the logical structure data table, such that thelogical structure data table may be established according to the massdata of the lexical items.

The keyword, the logic tag and the answer data may be extracted from thelexical item as follows.

Keyword: the title of the lexical item may be regarded as the keyword.For example, the title of a lexical item is “Luo Jin”, and then “LuoJin” may be regarded as one of the keywords. Alternatively, if the titleof the lexical item is too long, a core word and its closest adjectivesmay be extracted as a keyword. For example, if the title of the lexicalitem is “7.23

(tiger attacking event in Beijing Badaling safari park on July23^(rd))”, “

(tiger attacking event)” may be regarded as the keyword if the extractedcore word is “

(event)” and the closest adjective is “

(tiger attacking)”.

Specifically, a long title of a lexical item may be regarded as asentence when extracting the core word and the closest adjectives, andthe core word and the closest adjectives of the sentence may be acquiredafter a dependency syntax parsing and a semantic analysis are performedon the sentence. After the dependency syntax parsing and the semanticanalysis are performed on the sentence as shown in the above example,analyzed result may be acquired as illustrated in FIG. 5, in which linesand the above upper case letters represent dependency relationships, forexample, “HED” represents a head relation, “ATT” represents an attributerelation; a lower case letter represents an analyzing result of part ofspeech, for example, “m” represents a numeral, “w” represents apunctuation; and upper case letters at the bottom represent named entityidentifying results, for example, “NOR” represents a non-proper noun,and “LOC” represents a place name. Concerning other parts which are notillustrated, reference may be made to the description of the dependencyrelationship, the part of speech and the type of the named entity in therelated art. The core word (i.e., the word indicated by “HED”) may bedetermined according to the dependency relationship, for example, “

” in the above embodiment is a core word. The closest adjective of thecore word may be determined based on the dependency relationship, thepart of speech and the type of the named entity. For example, a nounwhich has the dependency relationship with the core word or a closestproper noun of the core word may be regarded as the closest adjective.

In FIG. 2, there is one keyword in each column of the logical structuredata table. It should be noted that the number of the keywords in onecolumn of the logical structure data table is not limited to one, theremay be a group of the keywords, which may be synonyms (such as “Leagueof Legends” and “LOL”).

Accordingly, other synonyms may be found after one keyword is extractedfrom the title of the lexical term, such that several synonyms may berecorded in one column of the logical structure data table. Any offollowing ways or the combination may be used for determining thesynonyms.

In a first way, the synonyms may be determined based on alternativenames of the lexical terms in the encyclopedia data. For example, if alexical term is entitled “A” and the alternative name of the lexicalterm is “B”, “A” and “B” are synonyms.

In a second way, the synonyms may be determined based on a synonymdictionary. For example, the synonyms may be determined based on thesynonyms recorded in the synonym dictionary.

In a third way, the synonyms may be determined based on the manuallabel. For example, the synonyms may be labeled by experts in the art.

In a fourth way, the synonyms may be determined based on automaticmining by a search engine. In this way, similar queries may be acquiredaccording to click situations in the search engine, for example, clickdistributions of “is it fun to play League of Legends” and “is it fun toplay LOL” are similar. Common parts of the two queries are deleted, andthe remaining parts may be a pair of potential synonyms.

Logic tag: the sub-title of the lexical term may be normalized to thepreset logic tag.

Original encyclopedia data are half-structured, which could beunderstood that there are several sub-titles (identified by HTML syntaxrules) of each lexical term. For example, for the lexical item “tigerattacking event in Beijing Badaling safari park on July 23^(rd)”,sub-titles such as “event background”, “event process”, “event results”and “event causes” etc. may be included. The sub-titles of the lexicalterm are normalized to the preset logic tags (the logic tags of lexicalitems in an event category may include “background”, “causes”,“process”, “latest developments” etc.). The normalization method is tocompute text similarities and to select a tag of which the similarity isgreatest and greater than a similarity threshold as the normalized tag.If the sub-title is unable to be normalized, the original sub-title maybe used as the logic tag. The abstract of the lexical term may be mappedto the logic tag “introduction” directly.

Answer data: the data in the sub-title corresponding to the logic tag ofa lexical term comprising the keyword may be regarded as the answer dataafter the keyword and the logic tag are determined.

As described above, the keyword, the logic tag and the answer data ofthe lexical item may be extracted respectively, in which the keyword,the logic tag and the answer data corresponding to one lexical item maybe used as an item in the logical structure data table, such that thelogical structure data table may be established according to the massdata of the lexical items.

In block S402, a core semanteme extracting model is established on theserver.

The core semanteme extracting model is used for extracting coresemanteme of the query. For example, the core semanteme of “what doesblue thin mushroom mean” or “who is Luojin” is “introduction”, the coresemanteme of “detail information about Nie Shubin's case” is “detailinformation”, and the core semanteme of “why the tiger attacking eventhappens” is “causes”.

Specifically, the core semanteme may be generated after training basedon low volume (such as less than a first predetermined threshold)labeled queries and massive (such as greater than a second predeterminedthreshold) non-labeled random queries. For a labeled query, the querymay be labeled by logic tags such as “introduction”, “causes”,“background” etc. For a non-labeled random query, frequency of thesemantic dependency relationship may be computed. Referring to FIG. 6and FIG. 7, the semantic dependency relationship “<SBV><-

(who is)” is included in both of “

(who is Luojin)” and “

(Do you know who is Fan Bingbing)”. From a perspective of whole randomqueries, the frequency of the semantic dependency relationship “<SBV><-

” is high and thus the potential value of this semantic dependencyrelationship is big. A key of the core semanteme extracting model is touse the massive non-labeled random queries to acquire a candidate set ofthe semantic dependency relationships with high frequencies, and toselect several suitable semantic dependency relationships and to labelthe suitable semantic dependency relationships with logic tags, suchthat the low volume labeled queries may be fitted in the case that thenon-labeled random queries are involved as many as possible. Theselected semantic dependency relationships and the logic tags attachedthereon may be the core semanteme extracting model.

It should be understood that block S401 and block S402 may bepre-established off-line so as to be applied in an online search processsubsequently. There is no order limitation to block S401 and block S402,which means that block S402 may be executed after block S401, or blockS401 may be executed after block S402, or block S401 and block S402 maybe executed in parallel.

In block S403, the query input by the user is received at the client.

The query may be input by the user in a form of text or speech.

In block S404, the query is sent by the client to the server.

In block S405, the query sent by the client is received by the server.

In block S406, the core semanteme of the query is extracted by theserver based on the core semanteme extracting model, in which the coresemanteme is used as the logic tag of the query.

The core semanteme extracting model is configured as a core semantemeonline service. The core semanteme of the original query may be acquiredbased on the semantic dependency relationship matching. It should benoted that not all of the queries may acquire the core semanteme, andthose queries without core semantemes may be abandoned.

In block S407, the keyword of the query is extracted by the server.

Following ways with priorities in a descending order may be used forextracting the keyword.

(1) The subject or an object related to the core semantic word directlymay be extracted as the keyword. For example, “

” is a core semantic word, and “

(Luo Jin)” and “

((Fan Bingbing)” related to “who” directly are regarded as the keywords.

(2) The proper noun having a closest editing distance with the coresemantic word in the query may be extracted as the keyword.

(3) The notional words including nouns and verbs may be extracted as thekeywords.

According to the above priority order, once one keyword is extracted,the extracted keyword may be deleted from the original query to avoidrepetitive extracting. In practice, there may be more than one keywordor no keyword at all.

It should be noted that the query received by the server may beconverted into a text if the query is not a text, and the logic tag andthe keyword corresponding to the query may be extracted thereafter toexecute subsequent steps.

It could be understood that there is no order limitation to block S406and block S407.

In block S408, the search result corresponding to the logic tag and thekeyword is acquired by the server based on a logical structure datatable.

The logical structure data table may be established by ordering theprocessed encyclopedia data in two dimensions of the keyword and thelogic tag. As shown in FIG. 2, the corresponding answer data isrepresented in the shade parts.

The corresponding answers may be acquired as the search results rapidlyby searching in use of the keyword and the logic tag extracted from thecurrent query. The answer may be selected according to priorities of thekeywords when more than one answer is acquired.

In block S409, the search result is sent to the client by the server.

In block S410, the search result is displayed to the user by the client.

It could be understood that the parts not described in detail in thisembodiment may refer to relative parts in relative embodiments.

In this embodiment, the search result may include images or texts, suchthat the search result may be illustrated for the user in more detail.The user may acquire the search result directly without any additionalexpenditure of time. The search result may be acquired without a URLprocessing, and may interact with the user in a natural dialogue manner.Therefore, the method is suitable in a voice-operated applicationwithout a screen. The search result is from the lexical terms of theencyclopedia data edited manually, which has a high quality, stabilityand authority.

FIG. 8 is a block diagram of an interactive apparatus based on deepquestion and answer according to an embodiment of the presentdisclosure.

As shown in FIG. 8, the interactive apparatus 80 includes: a receivingmodule 81, an acquiring module 82 and a displaying module 83.

The receiving module 81 is configured to receive a query.

The acquiring module 82 is configured to extract a logic tag of thequery, to extract a keyword of the query, and to acquire a search resultcorresponding to the logic tag and the keyword based on a logicalstructure data table.

The displaying module 83 is configured to display the search result.

In some embodiment, the search result includes an image and/or a text.

In some embodiment, referring to FIG. 9, the apparatus 80 also includes:a first establishing module 84 configured to establish the logicalstructure data table based on encyclopedia data. The first establishingmodule 84 is further configured to extract a keyword from a title of alexical item in the encyclopedia data, to select the keyword from thetitle or a combination of the keyword from the title and a synonymy ofthe keyword from the title as a keyword corresponding to the lexicalitem, and to normalize a sub-title of the lexical item to apredetermined logic tag; and to form the logical structure data tablebased on the keyword corresponding to the lexical item and thepredetermined logic tag normalized.

The keyword and the logic tag corresponding to the lexical term areconstructed into the logical structure data table.

In some embodiments, the acquiring module 82 is configured to extractthe logic tag of the query by extracting a core semanteme of the queryas the logic tag of the query based on a core semanteme extractingmodel.

In some embodiments, referring to FIG. 9, the apparatus 80 also includesa second establishing module 85, configured to establish the coresemanteme extracting model based on low volume labeled queries andmassive non-labeled random queries.

In some embodiments, the acquiring module 82 is configured to extractthe keywords of the query by: performing a dependency syntax parsing onthe query so as to acquire a core word of the query, and selecting aterm directly related to the core word as the keyword of the query; oracquiring from the query a proper noun having a closest editing distancewith a core word of the query, and selecting the proper noun as thekeyword of the query; or acquiring a content word in the query as thekeyword of the query.

It could be understood that the apparatus embodiments correspond to themethod embodiments, and the detail content of the apparatus embodimentsmay refer to the method embodiments, which will not be described indetail herein.

In this embodiment, since the search result rather than link informationis displayed, the user may see the search result without opening thewebpages, which may improve the search efficiency. Furthermore, sincethe search results may be acquired based on the logical structure datatable established based on encyclopedia data and the encyclopedia dataare authoritative, the precision of the search results may be improved.

It should be understood that the same or similar parts in theembodiments are just references to each other, and concerning thecontent which is not described in detail, reference may be made to thesame or similar parts in other embodiments.

Those skilled in the art shall understand that terms such as “first” and“second” are used herein for purposes of description and are notintended to indicate or imply relative importance or significance. Thus,the feature defined with “first” and “second” may comprise one or morethis feature. In the description of the present disclosure, “a pluralityof” means two or more than two, unless specified otherwise.

It will be understood that, the flow chart or any process or methoddescribed herein in other manners may represent a module, segment, orportion of code that comprises one or more executable instructions toimplement the specified logic function(s) or that comprises one or moreexecutable instructions of the steps of the progress. And the scope of apreferred embodiment of the present disclosure includes otherimplementations in which the order of execution may differ from thatwhich is depicted in the flow chart, which should be understood by thoseskilled in the art.

It should be understood that the various parts of the present disclosuremay be realized by hardware, software, firmware or combinations thereof.In the above embodiments, a plurality of steps or methods may be storedin a memory and achieved by software or firmware executed by a suitableinstruction executing system. For example, if it is realized by thehardware, likewise in another embodiment, the steps or methods may berealized by one or a combination of the following techniques known inthe art: a discrete logic circuit having a logic gate circuit forrealizing a logic function of a data signal, an application-specificintegrated circuit having an appropriate combination logic gate circuit,a programmable gate array (PGA), a field programmable gate array (FPGA),etc.

Those skilled in the art shall understand that all or parts of the stepsin the above exemplifying method of the present disclosure may beachieved by commanding the related hardware with programs. The programsmay be stored in a computer readable memory medium, and the programscomprise one or a combination of the steps in the method embodiments ofthe present disclosure when run on a computer.

In addition, each function cell of the embodiments of the presentdisclosure may be integrated in a processing module, or these cells maybe separate physical existence, or two or more cells are integrated in aprocessing module. The integrated module may be realized in a form ofhardware or in a form of software function modules. When the integratedmodule is realized in a form of software function module and is sold orused as a standalone product, the integrated module may be stored in acomputer readable memory medium.

The above-mentioned memory medium may be a read-only memory, a magneticdisc, an optical disc, etc.

Reference throughout this specification to “one embodiment”, “someembodiments,” “an embodiment”, “a specific example,” or “some examples,”means that a particular feature, structure, material, or characteristicdescribed in connection with the embodiment or example is included in atleast one embodiment or example of the present disclosure. Thus, theappearances of the phrases in various places throughout thisspecification are not necessarily referring to the same embodiment orexample of the present disclosure. Furthermore, the particular features,structures, materials, or characteristics may be combined in anysuitable manner in one or more embodiments or examples. In addition, ina case without contradictions, different embodiments or examples orfeatures of different embodiments or examples may be combined by thoseskilled in the art.

Although explanatory embodiments have been shown and described, it wouldbe appreciated that the above embodiments are explanatory and cannot beconstrued to limit the present disclosure, and changes, alternatives,and modifications can be made in the embodiments without departing fromscope of the present disclosure by those skilled in the art.

What is claimed is:
 1. An interactive method based on deep question andanswer, comprising: receiving a query; extracting a logic tag of thequery, extracting a keyword of the query, and acquiring a search resultcorresponding to the logic tag and the keyword based on a logicalstructure data table; and displaying the search result.
 2. Theinteractive method according to claim 1, wherein the search resultcomprises at least one of an image and a text.
 3. The interactive methodaccording to claim 1, further comprising: establishing the logicalstructure data table based on encyclopedia data, comprising: extractinga keyword from a title of a lexical item in the encyclopedia data,selecting the keyword from the title or a combination of the keywordfrom the title and a synonymy of the keyword from the title as a keywordcorresponding to the lexical item, and normalizing a sub-title of thelexical item to a predetermined logic tag; and forming the logicalstructure data table based on the keyword corresponding to the lexicalitem and the predetermined logic tag normalized.
 4. The interactivemethod according to claim 1, wherein extracting a logic tag of the querycomprises: extracting a core semanteme of the query as the logic tag ofthe query based on a core semanteme extracting model.
 5. The interactivemethod according to claim 4, further comprising establishing the coresemanteme extracting model comprising: establishing the core semantemeextracting model based on low volume labeled queries and massivenon-labeled random queries.
 6. The interactive method according to claim1, wherein extracting a keyword of the query comprises: performing adependency syntax parsing on the query so as to acquire a core word ofthe query, and selecting a term directly related to the core word as thekeyword of the query.
 7. The interactive method according to claim 1,wherein extracting a keyword of the query comprises: acquiring from thequery a proper noun having a closest editing distance with a core wordof the query, and selecting the proper noun as the keyword of the query.8. The interactive method according to claim 1, wherein extracting akeyword of the query comprises: acquiring a content word in the query asthe keyword of the query.
 9. An interactive apparatus based on deepquestion and answer, comprising: one or more processors; a memorystoring instructions executable by the one or more processors; whereinthe one or more processors are configured to: receive a query; extract alogic tag of the query, extract a keyword of the query, and acquire asearch result corresponding to the logic tag and the keyword based on alogical structure data table; and display the search result.
 10. Theinteractive apparatus according to claim 9, wherein the search resultcomprises at least one of an image and a text.
 11. The interactiveapparatus according to claim 9, wherein the one or more processors arefurther configured to establish the logical structure data table basedon encyclopedia data by acts of: extracting a keyword from a title of alexical item in the encyclopedia data, selecting the keyword from thetitle or a combination of the keyword from the title and a synonymy ofthe keyword from the title as a keyword corresponding to the lexicalitem, and normalizing a sub-title of the lexical item to a predeterminedlogic tag; and forming the logical structure data table based on thekeyword corresponding to the lexical item and the predetermined logictag normalized.
 12. The interactive apparatus according to claim 9,wherein the one or more processors are further configured to extract alogic tag of the query by an act of: extracting a core semanteme of thequery as the logic tag of the query based on a core semanteme extractingmodel.
 13. The interactive apparatus according to claim 12, wherein theone or more processors are further configured to establish the coresemanteme extracting model based on low volume labeled queries andmassive non-labeled random queries.
 14. The interactive apparatusaccording to claim 9, wherein the one or more processors are configuredto extract a keyword of the query by acts of: performing a dependencysyntax parsing on the query so as to acquire a core word of the query,and selecting a term directly related to the core word as the keyword ofthe query.
 15. The interactive apparatus according to claim 9, whereinthe one or more processors are configured to extract a keyword of thequery by an act of: acquiring from the query a proper noun having aclosest editing distance with a core word of the query, and selectingthe proper noun as the keyword of the query.
 16. The interactiveapparatus according to claim 9, wherein the one or more processors areconfigured to extract a keyword of the query by an act of: acquiring acontent word in the query as the keyword of the query.
 17. Anon-transitory computer-readable storage medium having stored thereininstructions that, when executed by a processor of a device, cause theprocessor to perform an interactive method based on deep question andanswer, the interactive method comprising: receiving a query; extractinga logic tag of the query, extracting a keyword of the query, andacquiring a search result corresponding to the logic tag and the keywordbased on a logical structure data table; and displaying the searchresult.